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Ai-900 > Exam > Flashcards

Exam Flashcards

1
Q

Forecasting housing prices based on historical data is an example of anomaly detection?

A

No

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2
Q

Identifying suspicious sign-ins by looking for deviations from usual pattern is an example of anomaly detection?

A

Yes

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3
Q

Predicting whether a pantient will develop diabetes based on the patient’s medical history is an example of anomaly detection?

A

No

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4
Q

The handling is unusual or missing values provided to an AI system is a consideration for the Microsoft ___ principle for responsible AI

A

Reliability and Safety

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5
Q

Match AI Workload to Scenario

  • Automated chat to answer questions about refunds and exchanges
  • Determining whether a photo contains a person
  • Determining whether a review is positive or negative
A
  • Conversational AI
  • Computer vision
  • NLP
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6
Q

Principle?

An AI system that empowers everyone, including people who have hearing, visual and other impairments

A

Inclusiveness

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7
Q

Principle:
Ensure that AI systems operate as they were originally designed, respond to unanticipated conditions and resist harmful manipulation

A

Reliability and safety

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8
Q

Principle:

Implementing processes to ensure that decisions made by AI systems can be overridden by humans

A

Accountability

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9
Q

Principle:

Provide consumers with information and controls over the collection, use and storage of their data

A

Privacy and security

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10
Q

Principle:
When developing an AI system for self-driving cars, the Microsoft responsible AI should be applied to ensure consistent operation system during unexpected circumstances

A

Reliability and safety

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11
Q

Which services should you use to extract text, key/value pairs and table data automatically from scanned documents?

  • Form Recongizer
  • Text Analytics
  • Ink Recognizer
  • Custom Vision
A
  • FORM RECOGNIZER
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12
Q

The ability to extract subtotals and totals from a receipt is a capability of the ___ service

A

Form Recognizer

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13
Q

You use Azure Machine Learning designer to publish an inference pipeline.

Which two parameters should you use to consume the pipeline? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. the model name
B. the training endpoint
C. the authentication key
D. the REST endpoint

A

C. the authentication key

D. the REST endpoint

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14
Q

From Azure Machine Learning Designer, to deploy a real-time inference pipeline as a services for others to consume, you must deploy the model to

  • a local web service
  • Azure Container Instances
  • Azure Kubernetes Services
  • Azure Machine Learning compute
A

Azure Kubernetes Service

NOTE:
For Production use AKS
For Dev/Text use Azure Container Instances

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15
Q

Predicting how many hours of overtime a delivery person will work based on the number of order received in an example of ____

  • Classification
  • Clustering
  • Regression
A
  • Regression

NOTE:

In the most basic sense, regression refers to prediction of a numeric target.

Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable.
You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.
Incorrect Answers:
✑ Classification is a machine learning method that uses data to determine the category, type, or class of an item or row of data.
✑ Clustering, in machine learning, is a method of grouping data points into similar clusters. It is also called segmentation.
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16
Q

Difference between classification, clustering and regression?

A 
✑ Classification is a machine learning method that uses data to determine the category, type, or class of an item or row of data.
✑ Clustering, in machine learning, is a method of grouping data points into similar clusters. It is also called segmentation.
✑ Regression, in the most basic sense, refers to prediction of a numeric target
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17
Q

Yes or No:

  • Azure Machine Learning designer provides a drag-and-drop visual canvas to build, test and deploy machine Learning models?
  • Azure Machine Learning designer enables you to save your progress as a pipeline draft
  • Azure Machine Learning designer enables you to include custom JavaScript functions
A

Box 1: Yes -
Azure Machine Learning designer lets you visually connect datasets and modules on an interactive canvas to create machine learning models.

Box 2: Yes -
With the designer you can connect the modules to create a pipeline draft.
As you edit a pipeline in the designer, your progress is saved as a pipeline draft.

Box 3: No -
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designe

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18
Q

Household Income:
20 000
23 000
80 000

House Price Category:
Low
Medium
High

Which is a feature and which is a label?

A

Household Income = Feature
House Price Category = Label

NOTE:
Feature = Input
Label = Output

19
Q

Which metric can you use to evaluate a classification model?

A. true positive rate
B. mean absolute error (MAE)
C. coefficient of determination (R2)
D. root mean squared error (RMSE

A

A. True Positive Rate

What does a good model look like?

An ROC curve that approaches the top left corner with 100% true positive rate and 0% false positive rate will be the best model.

A random model would display as a flat line from the bottom left to the top right corner.

Worse than random would dip below the y=x line

20
Q

Which two components can you drag onto a canvas in Azure Machine Learning designer?

A. dataset
B. compute
C. pipeline
D. module

A

A. Dataset

D. Module

21
Q 
You need to create a training dataset and validation dataset from an existing dataset.
Which module in the Azure Machine Learning designer should you use?

A. Select Columns in Dataset
B. Add Rows
C. Split Data
D. Join Data

A

C. Split Data

A common way of evaluating a model is to divide the data into a training and test set by using Split Data, and then validate the model on the training data.
Use the Split Data module to divide a dataset into two distinct sets.
The studio currently supports training/validation data splits
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-configure-cross-validation-data-splits

22
Q

Match the types of machine learning to the appropriate scenarios.

  • Predict how many minutes late a flight will arrive based on the amount of snowfall at an airport
  • Segment customers into different groups to support a marketing department
  • Predict whether aa student will complete a university course
A

Box 1: Regression -
In the most basic sense, regression refers to prediction of a numeric target.
Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable.
You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.

Box 2: Classification -
Classification is a machine learning method that uses data to determine the category, type, or class of an item or row of data.

Box 3: Clustering - (Similarities)
Clustering, in machine learning, is a method of grouping data points into similar clusters. It is also called segmentation.
Over the years, many clustering algorithms have been developed. Almost all clustering algorithms use the features of individual items to find similar items. For example, you might apply clustering to find similar people by demographics. You might use clustering with text analysis to group sentences with similar topics or sentiment

23
Q

Match the machine learning tasks to the appropriate scenarios.

  • Examining the values of a confusion matrix
  • Splitting a date into month, day, and year fields
  • Picking temperature and pressure to train a weather model

Choose from Learning Types:

  • Feature engineering
  • Feature selection
  • Model Deployment
  • Model evaluation
  • Model training
A

Answer:

Box 1: Model evaluation -
The Model evaluation module outputs a confusion matrix showing the number of true positives, false negatives, false positives, and true negatives, as well as
ROC, Precision/Recall, and Lift curves.

Box 2: Feature engineering -
Feature engineering is the process of using domain knowledge of the data to create features that help ML algorithms learn better. In Azure Machine Learning, scaling and normalization techniques are applied to facilitate feature engineering. Collectively, these techniques and feature engineering are referred to as featurization.
Note: Often, features are created from raw data through a process of feature engineering. For example, a time stamp in itself might not be useful for modeling until the information is transformed into units of days, months, or categories that are relevant to the problem, such as holiday versus working day.

Box 3: Feature selection -
In machine learning and statistics, feature selection is the process of selecting a subset of relevant, useful features to use in building an analytical model. Feature selection helps narrow the field of data to the most valuable inputs. Narrowing the field of data helps reduce noise and improve training performance

24
Q

Data values that influence the prediction of a model are called __

  • dependent variables
  • features
  • identifiers
  • labels
A

Answer:

  • Labels
    In machine learning, if you have labeled data, that means your data is marked up, or annotated, to show the target, which is the answer you want your machine learning model to predict.
    In general, data labeling can refer to tasks that include data tagging, annotation, classification, moderation, transcription, or processing.

Incorrect Answers:
Not features: In machine learning and statistics, feature selection is the process of selecting a subset of relevant, useful features to use in building an analytical model. Feature selection helps narrow the field of data to the most valuable inputs. Narrowing the field of data helps reduce noise and improve training performance

25
Q

What is a Predicted vs. True chart?

A

Predicted vs. True shows the relationship between a predicted value and its correlating true value for a regression problem. This graph can be used to measure performance of a model as the closer to the y=x line the predicted values are, the better the accuracy of a predictive model.

26
Q

Which type of machine learning should you use to predict the number of gift cards that will be sold next month?

A. classification
B. regression
C. clustering

A
  • Clustering, in machine learning, is a method of grouping data points into similar clusters. It is also called segmentation.
    Over the years, many clustering algorithms have been developed. Almost all clustering algorithms use the features of individual items to find similar items. For example, you might apply clustering to find similar people by demographics. You might use clustering with text analysis to group sentences with similar topics or sentiment
27
Q

You have a dataset that contains information about taxi journeys that occurred during a given period.
You need to train a model to predict the fare of a taxi journey.

What should you use as a feature?
A. the number of taxi journeys in the dataset
B. the trip distance of individual taxi journeys
C. the fare of individual taxi journeys
D. the trip ID of individual taxi journeys

A

B. the trip distance of individual taxi journeys

The label is the column you want to predict. The identified Features are the inputs you give the model to predict the Label.

28
Q
  • You need to predict the sea level in meters for the next 10 years.
    Which type of machine learning should you use?
    A. classification
    B. regression
    C. clustering
A

B. regression

In the most basic sense, regression refers to prediction of a numeric target.
Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable.
You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions
29
Q

Yes/No

  • Automated machine learning is the process of automating the time-consuming, iterative tasks of machine learning model development.
  • Automated machine learning can automatically infer the training data from the use case provided
  • Automated machine learning works by running multiple training iterations that are scored and ranked by the metrics you specify
  • Automated machine learning enables you to specify a dataset and will automatically understand which label to predict
A
  • Yes
  • No
  • Yes
  • No
30
Q

A banking system that predicts whether a loan will be repaid is an example of the ___ type of machine learning

  • classification
  • regression
  • clustering
A
  • classification
31
Q

Yes/No

  • Labelling is the process of tagging training data with known values
  • You should evaluate a model by using the same data used to train the model
  • Accuracy is always the primary metric used to measure a models performance
A

Box 1: Yes -
In machine learning, if you have labeled data, that means your data is marked up, or annotated, to show the target, which is the answer you want your machine learning model to predict.
In general, data labeling can refer to tasks that include data tagging, annotation, classification, moderation, transcription, or processing.

Box 2: No -

Box 3: No -
Accuracy is simply the proportion of correctly classified instances. It is usually the first metric you look at when evaluating a classifier. However, when the test data is unbalanced (where most of the instances belong to one of the classes), or you are more interested in the performance on either one of the classes, accuracy doesn’t really capture the effectiveness of a classifier

32
Q

You need to develop a mobile app for employees to scan and store their expenses while travelling.
Which type of computer vision should you use?
A. semantic segmentation
B. image classification
C. object detection
D. optical character recognition (OCR)

A

D. optical character recognition (OCR)

33
Q
  • Do two images of a face belong to the same person?
  • Does this person look like other people?
  • Do all the faces belong together?
  • Who is this person in this group of people?
A
  • verification
  • similarity
  • grouping
  • identification
34
Q

Computer vision to appropriate scenarios:

  • Identify celebrities in images
  • Extract movie title names from movie poster images
  • Locate vehicles in images

Choose from:

  • Facial recognition
  • Image classification
  • Object detection
  • Optical character recognition
A
  • Facial recognition
  • Optical character recognition
  • Object detection
35
Q

You need to determine the location of cars in an image so that you can estimate the distance between the cars.

Which type of computer vision should you use?

A. optical character recognition (OCR)
B. object detection
C. image classification
D. face detection

A

B. object detection

Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found

36
Q

You can use the ___ service to train an object detection model by using your own images

  • Computer Vision
  • Custom Vision
  • Form Recognizer
  • Video Indexer
A

Azure Custom Vision is a cognitive service that lets you build, deploy, and improve your own image classifiers. An image classifier is an AI service that applies labels (which represent classes) to images, according to their visual characteristics. Unlike the Computer Vision service, Custom Vision allows you to specify the labels to apply.

37
Q

What are two tasks that can be performed by using the Computer Vision service? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. Train a custom image classification model.
B. Detect faces in an image.
C. Recognize handwritten text.
D. Translate the text in an image between languages.

A

B. Detect faces in an image.

C. Recognize handwritten text.

38
Q

What is a use case for classification?
A. predicting how many cups of coffee a person will drink based on how many hours the person slept the previous night.
B. analyzing the contents of images and grouping images that have similar colors
C. predicting whether someone uses a bicycle to travel to work based on the distance from home to work
D. predicting how many minutes it will take someone to run a race based on past race times

A

A refers to regression.
B refers to Clustering
C refers to CLASSIFICATION
D refers to Regression

39
Q

What are two tasks that can be performed by using computer vision? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. Predict stock prices.
B. Detect brands in an image.
C. Detect the color scheme in an image
D. Translate text between languages.
E. Extract key phrases.

A

B. Detect brands in an image.

C. Detect the color scheme in an image

40
Q

Your company wants to build a recycling machine for bottles. The recycling machine must automatically identify bottles of the correct shape and reject all other items.
Which type of AI workload should the company use?
A. anomaly detection
B. conversational AI
C. computer vision
D. natural language processing

A

C. computer vision

41
Q

Your website has a chatbot to assist customers.
You need to detect when a customer is upset based on what the customer types in the chatbot.
Which type of AI workload should you use?
A. anomaly detection
B. semantic segmentation
C. regression
D. natural language processing

A

D. natural language processing

42
Q

NLP can be used to:

  • classify email messages as work-related or personal
  • predict the number of future car rentals
  • predict which website visitors will make a transaction
  • stop a process in a factory when extremely high temperature are registered
A
  • classify email messages as work-related or personal
43
Q 
Which AI service can you use to interpret the meaning of a user input such as "Call me back later?"
A. Translator Text
B. Text Analytics
C. Speech
D. Language Understanding (LUIS)
A

Text Analytics is an AI service that uncovers insights such as sentiment, entities, and key phrases in unstructured text

44
Q

You are developing a chatbot solution in Azure.
Which service should you use to determine a user’s intent?
A. Translator Text
B. QnA Maker
C. Speech
D. Language Understanding (LUIS)

A

Language Understanding (LUIS) is a cloud-based API service that applies custom machine-learning intelligence to a user’s conversational, natural language text to predict overall meaning, and pull out relevant, detailed information.

Ai-900 (1 decks)

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